基于动态VaR模型和Copula函数的省级政府平台公司投资风险测度

省级政府投融资平台公司在城镇化建设中发挥着重要作用,其投融资风险问题亦逐渐引起重视。基于平台公司的投资收益会随着市场等宏观环境的变化而波动的考虑,将平台公司的项目投资资产视为金融资产而度量其风险状况。考虑单笔投资的情形,建立极值理论和SV-t模型的相结合平台公司一维融资风险动态VaR模型;进而考虑多笔投资间的非线性关系,结合Copula函数和蒙特卡洛模拟思路,建立平台公司多维融资风险度量模型。所构建的模型避免了传统研究的强主观性,并实现了投资风险的实时、动态度量。     

Risk dependence and cointegration between pharmaceutical stock markets: The case of China and the USA

This paper analyses risk-integration and the degree of dependence between the Values-at-Risk (VaRs) estimates for the two major pharmaceutical stock markets in the world: USA and China. To do this, we study the dependence and fractional cointegration properties among risks. Using daily returns for an eleven-year period, we estimated the VaRs obtained for pharmaceutical market portfolios in China (Shanghai) and the USA (NYSE) using the market model and considering both long and short trading positions. We conclude that the Shanghai pharmaceutical market is riskier than NYSE, although is predictable and losses in both markets exhibit tail dependence between VaR estimates. Particularly, there is lower tail VaR dependence for long position and upper tail dependence for short positions, both being small and fairly constant. On the other hand, we have not found fractional cointegration between risks, suggesting that China’s pharmaceutical sector is not integrated into the global pharmaceutical market.     

A Copula Nonlinear Granger Causality

We propose a new copula nonlinear Granger causality test that is more robust than the current available linear and nonlinear Granger causality tests when there exists an asymmetric and nonlinear directional dependence. To perform the statistical test of the copula nonlinear causality, the Gaussian Copula Marginal Regression (GCMR) model and copula directional dependence (Kim and Hwang, 2017) are employed in this paper. By using GCMR and two-sample permutation test with rank sum statistic for the copula nonlinear Granger causality, we can confirm that the result of the proposed copula nonlinear Granger causality test is a reliable test through the simulated data and real data both for small and large sample sizes.     

The joint effects of financial development and the business environment on firm growth: Evidence from Vietnam

This work examines the complementary effects of local financial development and the business environment on the growth of Vietnamese firms. For the period from 2009 to 2013, we combine firm-level data covering more than 40,000 firms from the Vietnam Enterprise Survey with province-level data from the Vietnam Provincial Competitiveness Indicators. Our estimation strategy builds upon a novel copula-based estimator that accounts for potential endogeneity biases without requiring external instruments. Our results show that financial development and a favourable business environment generally promote firm growth, but some components of the business environment, such as low entry costs, access to land and business service support, foster firm growth more strongly than financial development. Most importantly, financial development and the business environment interact positively in their effects on firm growth. The impact of local financial development on firm growth is higher in provinces with a competitive business environment. Conversely, improvements in provincial competitiveness have a greater impact on firm growth in provinces with a more developed financial sector. The results clearly show that policies to promote local financial development need to be coordinated with measures to improve the broader business environment.     

基于Copula-GARCH对上证和深证的相关性分析

文章首先利用GARCH模型,估计出单个资产收益率在将来某个时刻的条件概率分布;其次,运用Copula理论,构造出Copula函数,刻画投资组合中不同资产间的相关结构,得到两个资产的联合分布;最后应用Copula-GARCH模型对上证综指和深证成指的相关性进行分析。     

Portfolio optimization with CVaR under VG process

Formal portfolio optimization methodologies describe the dynamics of financial instruments price with Gaussian Copula (GC). Without considering the skewness and kurtosis of assets return rate, optimization with GC underestimate the optimal CVaR of portfolio. In the present paper, we develop the approach for portfolio optimization by introducing Lévy processes. It focuses on describing the dynamics of assets’ log price with Variance Gamma copula (VGC) rather than GC. A case study for three Indexes of Chinese Stock Market is performed. On application purpose, we calculate the best hedge positions of Shanghai Index (SHI), Shenzhen Index (SZI) and Small Cap Index (SCI) with the performance function CVaR under VG model. It can be combined with Monte Carlo Simulation and nonlinear programming techniques. This framework is suitable for any investment companies.     

时变Copula模型的非参数推断

运用Copula模型研究金融变量之间的相关结构,是近年来金融分析中的一个热点,如何估计Copula模型中的时变参数则是一个重点和难点问题。本文从非参数建模思想为切入点,提出经验分布函数—局部极大似然法(ECDF-LML)估计Copula函数中的时变参数,研究了Copula模型参数是否时变的统计假设检验问题。最后通过大量随机模拟研究验证了本文所提出的方法较DCC-MGARCH方法在刻画随机变量动态相关性方面更具优越性且很稳健。     

基于相依违约的上市公司担保风险度量研究

本文运用基于相依违约的混合模型度量上市公司担保风险,并进行了实证研究。结果表明:此模型能很好的预测上市公司对外担保的违约概率,可对上市公司信用进行评级;在敏感性分析中,违约概率对波动率、无风险利率和相依结构比较敏感,这能为风险管理提供一定的参考。     

居民家庭金融资产组合集成风险的测量与波动分析

居民家庭金融资产在不同时期风险的聚集可能会引发宏观金融风险, 甚至导致金融危机。 因此, 文章试图测算家庭金融资产组合风险并描述其变动特点。 首先构建家庭金融资产组合的 Copula 函数, 然后计算其VaR 值, 并比较分析 VaR 值与各金融资产收益的变动关系 , 发现当金融资产中高风险资产的收益低于 VaR 值下限时, 家庭金融资产风险不断积聚并达到高点, 而这个过程与金融危机发生的时间相契合。 家庭金融资产组合风险和资产中的风险资产收益会影响未来的利率和 CPI 的变化。     

两类混合藤Copula模型的比较研究——基于外汇资产投资组合VaR的研究

本文以中国外汇市场上四种主要外汇资产的投资组合作为研究对象,基于Pair Copula高维建模思想,分别建立了两类能真实反映资产组合相关结构差异性的混合藤Copula模型,即混合C藤和混合D藤Copula模型。两类混合藤Copula模型,对传统的藤Copula模型作了进一步的改进,是通过一定的选择标准,确定模型中每个Pair Copula函数的最优函数族,这样可以使得所建立的模型既能考虑资产组合维数的影响,又能捕捉到组合内部各资产相关结构的差异性。为了得到较优的风险分析模型,在实证研究中,将两类模型在资产组合VaR计算精度方面进行比较。